In lending, loan portfolios are fraught with inherent risk because the future is unknown. The institutions that can best manage this risk, and, in essence, predict the future, are the ones most suited to succeed. As a result, lending institutions, in an attempt to minimize this risk, have devised numerous models and methods to try to approximate the future or, at the very least, research the past as an indicator of the future. The greatest difficulty in developing these methods and models is in choosing an appropriate range of possible futures and correctly assigning probabilities of occurrence to them. The problem is that the future of a loan portfolio, like many other problems, is path dependent. Given a set of possible events, it may be impossible for them all to occur because of mutual exclusivity. We cannot have a robust economy driving a booming portfolio next month if we are in the depths of a recession this month.
The amount of available data also creates limitations that must be dealt with. The standard caveat in stock trading is that past returns many not reflect future performance. The same is true when considering the time series of a loan portfolio's performance. The Asian Economic Crisis is a prime example, where 10 years of smooth steady performance did not represent the range of possible futures because the portfolios themselves had shifted to a much higher risk posture during that time. The risk distribution represented by past performance was excluded by current conditions.
Commercial lenders have developed tools over the years that help limit this lending risk. Typically, risk ratings are assigned to commercial borrowers by risk rating agencies, and these risk ratings have worked relatively well in assisting lenders in assessing commercial lending risks. These risk ratings are based on the assumptions that commercial loans are large loans and very few in number and that broad market and balance sheet intelligence exist on the borrower. Retail (consumer) lenders have not enjoyed such equivalent success in risk management.
In current standard practices, retail lenders generate loss forecasts for consumer loans by estimating the next year's expected loss and the deviation about the loss forecast. The loss forecast is usually called the expected loss (EL) and the deviation in losses is called the unexpected loss (UL). Expected losses may be set via internal forecasting processes or simply taken as equal to the previous year. Unexpected losses are usually computed directly from observed historical performance.
For both EL and UL, retail lending institutions know that past performance is not an ideal indicator of future performance because of changes in the portfolio: different demographic mix; different subproduct mix; changes in originations; and credit policy changes. In addition, the economic and competitive environment can change dramatically. Given the few years of data available to the typical institution, the observed historical performance will not capture the breadth of possible economic environments.
Although retail lenders recognize these shortcomings, they still use approaches that do not really address them. The two main approaches that retail lenders use are: (1) Monte Carlo simulation of portfolio performance and (2) industry comparisons. Monte Carlo simulation is a method of trying many possible randomly generated futures. In general, Monte Carlo is a well-known and useful technology, but when applied to total portfolio performance, it is unable to account for any of the portfolio and environmental changes mentioned above. Industry comparison is a logical approach to bolstering the limited internal data by looking at performance of other retail lending institutions. Unfortunately, industry comparison is crude at best because there is no clear approach to calibrating industry-wide data to an individual retail lending institution's portfolio. The industry average can be changing in composition as well as the individual portfolio.
In light of these shortcomings, many organizations have tried to use the tools developed for commercial lending. Those tools, however, are not suitable for application to retail lending because the underlying factors that make them work for commercial lending (i.e., small numbers of large loans, broad market and balance sheet intelligence on the borrower) do not carryover to retail lending. As a result, the leading retail lending institutions do not consider these tools very useful.
Another frequently tried and failed approach is to aggregate account-level scores to the total portfolio. The difficulty is that almost all scores created are rank orderings of customers, not predictions of specific levels of revenue and loss. When the mapping is attempted between scores and loss levels, difficulties arise with the changing environment. Account-level modeling can work for creating loss distributions, but it must begin with a technology, like dual-time dynamics (described below), capable of incorporating consumer lifecycles and the changing external environment.
The standard approaches have such low fidelity that securitization of credit card receivables does not use the typical ABS (Asset-backed Securities) structure of creating different risk tranches. Rather, a single risk pool is created. Accurate forecasting of the distribution of possible losses would make an ABS structure possible. Furthermore, the proposed Basel II Accord for setting economic capital and changes by US regulators highlights the need for new approaches to computing loan loss reserves and economic capital.